Neural Networks Questions and Answers – Introduction of Feedback Neural Network

This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Introduction Of Feedback Neural Network″.

1. How can false minima be reduced in case of error in recall in feedback neural networks?
a) by providing additional units
b) by using probabilistic update
c) can be either probabilistic update or using additional units
d) none of the mentioned
View Answer

Answer: b
Explanation: Hard problem can be solved by additional units not the false minima.

2. What is a Boltzman machine?
a) A feedback network with hidden units
b) A feedback network with hidden units and probabilistic update
c) A feed forward network with hidden units
d) A feed forward network with hidden units and probabilistic update
View Answer

Answer: b
Explanation: Boltzman machine is a feedback network with hidden units and probabilistic update.

3. What is objective of linear autoassociative feedforward networks?
a) to associate a given pattern with itself
b) to associate a given pattern with others
c) to associate output with input
d) none of the mentioned
View Answer

Answer: a
Explanation: The objective of linear autoassociative feedforward networks is to associate a given pattern with itself.
advertisement

4. Is there any error in linear autoassociative networks?
a) yes
b) no
View Answer

Answer: b
Explanation: Because input comes out as output.

5. If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output in case of autoassociative feedback network?
a) a(l)
b) a(l) + e
c) could be either a(l) or a(l) + e
d) e
View Answer

Answer: b
Explanation: This is due to the absence of accretive behaviour.
Free 30-Day C++ Certification Bootcamp is Live. Join Now!

6. If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is accretive in nature?
a) a(l)
b) a(l) + e
c) could be either a(l) or a(l) + e
d) e
View Answer

Answer: a
Explanation: This is the property of accretive system.

7. If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is interpolative in nature?
a) a(l)
b) a(l) + e
c) could be either a(l) or a(l) + e
d) e
View Answer

Answer: b
Explanation: This is the property of interpolative system.

8. What property should a feedback network have, to make it useful for storing information?
a) accretive behaviour
b) interpolative behaviour
c) both accretive and interpolative behaviour
d) none of the mentioned
View Answer

Answer: a
Explanation: During recall accretive behaviour make it possible for system to store information.

9. What is the objective of a pattern storage task in a network?
a) to store a given set of patterns
b) to recall a give set of patterns
c) both to store and recall
d) none of the mentioned
View Answer

Answer: c
Explanation: The objective of a pattern storage task in a network is to store and recall a given set of patterns.
advertisement

10. Linear neurons can be useful for application such as interpolation, is it true?
a) yes
b) no
View Answer

Answer: a
Explanation: This means for input vector x, output vector y is produced and for input a.x, output will be a.y.

Sanfoundry Global Education & Learning Series – Neural Networks.

To practice all areas of Neural Networks, here is complete set on 1000+ Multiple Choice Questions and Answers.

advertisement
advertisement
Subscribe to our Newsletters (Subject-wise). Participate in the Sanfoundry Certification contest to get free Certificate of Merit. Join our social networks below and stay updated with latest contests, videos, internships and jobs!

Youtube | Telegram | LinkedIn | Instagram | Facebook | Twitter | Pinterest
Manish Bhojasia - Founder & CTO at Sanfoundry
I’m Manish - Founder and CTO at Sanfoundry. I’ve been working in tech for over 25 years, with deep focus on Linux kernel, SAN technologies, Advanced C, Full Stack and Scalable website designs.

You can connect with me on LinkedIn, watch my Youtube Masterclasses, or join my Telegram tech discussions.

If you’re in your 40s–60s and exploring new directions in your career, I also offer mentoring. Learn more here.